moraldilemma: Moral Dilemma Judgment
In mpt: Multinomial Processing Tree Models

moraldilemma

R Documentation

Moral Dilemma Judgment

Description

Hennig and Huetter (2020) proposed a multinomial model of moral dilemma judgment and evaluated the model in a series of experiments. Participants were presented with hypothetical scenarios that required a decision whether or not to break a moral norm.

Berentelg (2020) conducted a replication study that was designed to be similar to Experiment 2b in Hennig and Huetter (2020).

Usage

data(moraldilemma)

Format

MDHennig2020 A data frame consisting of seven variables:

selfrel: factor. Instructions about self-relevant consequences of the decision where either (absent) or (present).
congrcy: factor. Endorsement of consequences and norm endorsement lead to different decisions (incongruent) or to the same decision (congruent).
default: factor. The norm may be adhered to by continuing (inaction default state) or by changing (action default state) an ongoing behavior.
breaknorm: factor. Decision to break the norm.
exp1, exp2b: the aggregate response frequencies for Experiment 1 and 2b, respectively.
treeid: an identifier for the single trees of the joint multinomial model.

MDreplication A data frame containing 751 observations of five variables:

selfrel: factor. See above.
gender: factor. Participant gender.
age: participant age.
rt: median response time (in seconds) across scenarios.
y: a matrix of response frequencies per participant. Each column represents a combination of the factors congrcy, default, and breaknorm.

Source

Hennig, M., & Huetter, M. (2020). Revisiting the divide between deontology and utilitarianism in moral dilemma judgment: A multinomial modeling approach. Journal of Personality and Social Psychology, 118(1), 22–56. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/pspa0000173")}

Berentelg, M. (2020). Multinomial modeling of moral dilemma judgment: A replication study. Bachelor thesis. University of Tuebingen, Germany. https://osf.io/mb32t/

Examples

data(moraldilemma)

## Exp. 1: proCNI and process dissociation (PD) model
s <- mptspec("proCNI")
exp1 <- subset(MDHennig2020, selfrel == "absent")
mpt(update(s, .restr = list(J=I)),      data = exp1, freqvar = "exp1")
mpt(update(s, .restr = list(I=0, J=1)), data = exp1, freqvar = "exp1")

## Exp. 2b: self-relevant consequences and norm endorsement
s <- mptspec("proCNI", .replicates = 2, .restr = list(J1=I1, J2=I2))
m1 <- mpt(s, data = MDHennig2020, freqvar = "exp2b")
m2 <- mpt(update(m1$spec, .restr = list(N1=N2)), data = m1$y)
anova(m2, m1)

## Replication of Exp. 2b
md.agg <- aggregate(y ~ selfrel, MDreplication, sum)
y <- as.vector(t(md.agg[, -1]))

m3 <- mpt(s, data = y)
m4 <- mpt(update(s, .restr = list(N1=N2)), data = y)
anova(m4, m3)

coefs <- c(diff(coef(m3)[c("N2", "N1")]),
           diff(coef(m1)[c("N2", "N1")]))
names(coefs) <- c("Replication", "Hennig & Huetter\n(2020, Exp. 2b)")
ci <- coefs + rbind(
  qnorm(c(.025, .975))*sqrt(sum(diag(vcov(m3))[c("N2", "N1")])),
  qnorm(c(.025, .975))*sqrt(sum(diag(vcov(m1))[c("N2", "N1")]))
)
dotchart(coefs, pch = 16, xlim = c(-.2, 1),
  xlab = expression(N[absent] - N[present]~"(proCNI model, 95% CI)"),
  main = paste("Self-relevant consequences and norm endorsement",
               "in moral dilemma judgment", sep = "\n"))
abline(v = 0, col = "gray")
arrows(ci[, 1], 1:2, ci[, 2], 1:2, .05, 90, 3)

mpt documentation built on Oct. 11, 2024, 5:07 p.m.